Using AI at Work: AI in the Workplace & Generative AI for Business Leaders

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Most business owners know AI matters, but few know where to start.
In this episode Chris sits down with Corey Ganim, entrepreneur, AI consultant, and creator of AI assessment frameworks for small businesses. Corey shares how he helps non-technical business owners identify automation opportunities, reduce manual work, and implement practical AI solutions that generate measurable time savings. From AI skills and projects to voice agents and workflow automation, he explains how leaders can get results without becoming technical experts.
The conversation explores AI assessments, token efficiency, brand voice systems, AI-powered knowledge management, and the future of AI agents inside organizations. Corey also shares lessons learned building AI services, evaluating tools, and helping businesses create the data foundations required for long-term AI success. Leaders looking for practical AI implementation strategies will find actionable ideas they can apply immediately.

Chapters
(00:00) Introduction
(05:10) Becoming a Vibe Coder Without a Technical Background
(07:51) How Corey Evaluates and Creates AI Tool Content
(11:00) Why You Don't Need to Be an AI Expert to Create Value
(13:15) Choosing the Right AI Tool for the Job
(15:11) Skills, SOPs, and Repeatable AI Workflows
(17:10) Token Efficiency, Rate Limits, and Better AI Usage
(20:05) Building AI Projects and Custom Knowledge Systems
(28:09) Creating a Brand Voice Skill That Sounds Like You
(30:45) The AI Assessment Business Model Explained
(35:35) AI Assessments, Guarantees, and Time-Saving Opportunities
(42:09) Using Voice Agents to Conduct AI Discovery Calls
(45:32) Why Every Business Needs an Internal Knowledge Base
(49:58) The Future of AI Adoption and Autonomous Agents

🔎 Find Out More About Corey Ganim
Corey Ganim LinkedIn
https://www.linkedin.com/in/coreyganim
Corey Ganim Website
https://coreyganim.com
X (Twitter)
https://x.com/coreyganim
Build With AI Podcast:
https://podcasts.apple.com/us/podcast/build-with-ai/id1689819329 

🛠 AI Tools and Resources Mentioned:
Claude
https://claude.ai
ChatGPT
https://chatgpt.com
Claude Code
https://www.anthropic.com/claude-code
OpenAI Codex
https://openai.com/codex
Gamma
https://gamma.app
Google Gemini
https://gemini.google.com
Zapier
https://zapier.com
FutureTools
https://www.futuretools.io
Fathom
https://fathom.video
SaneBox
https://www.sanebox.com
TaxJar
https://www.taxjar.com
Retell AI
https://www.retellai.com
Twilio
https://www.twilio.com
ElevenLabs
https://elevenlabs.io
Notion
https://www.notion.so

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Voice AI is advancing faster than most organizations realize.
In this episode Chris talks with Shawn Zhang, CTO and co-founder of Sanas, the enterprise voice AI company focused on improving global communication through AI. Shawn shares how voice AI is evolving beyond call automation into a foundational layer for communication, context capture, and more natural human interaction with AI systems.
The conversation explores enterprise use cases, latency, customer service, sales applications, and what leaders should evaluate before investing in voice AI solutions. Executives exploring AI adoption will gain practical insight into where voice technology delivers value today and where the next wave of opportunity is emerging.

Chapters:
00:00 Introduction
02:27 Why Voice AI Matters for Business Leaders
04:47 Are We Early or Late to Voice AI?
06:06 Why Speech Is More Than Words
07:42 How Sanas Separates Voice Signals and Context
11:54 Enterprise Use Cases for Voice AI
15:23 Security, Local AI, and Enterprise Deployment
21:16 How Leaders Should Evaluate Voice AI Vendors
32:00 Why Latency Changes User Experience

Resources:
🔎 Find Out More About Shawn Zhang:

Shawn Zhang LinkedIn
https://www.linkedin.com/in/shawnbzhang
Sanas 
https://www.sanas.ai

🛠 AI Tools and Resources Mentioned:

OpenAI
https://openai.com

ChatGPT
https://chat.openai.com

Block
https://block.xyz

AIR.AI
https://air.ai

Klarna 
https://www.klarna.com

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Most companies are using AI, but very few are redesigning work around it.
In this episode Chris sits down with Karl Simon, co-founder and CTO of Subatomic, an AI workflow orchestration company, to explore why task based AI adoption is limiting business impact. They discuss the shift from isolated AI use cases toward unified workflows powered by clean data, AI coworkers, and cross functional orchestration. The conversation also explores how organizations may flatten hierarchies as AI takes over information movement and decision support responsibilities.
Chris and Karl unpack practical steps leaders can take to move from experimentation into operational transformation, including workflow discovery, data readiness, security, and ROI prioritization. Leaders looking to move beyond AI pilots and toward business redesign will find this episode especially valuable.

Chapters:
00:00 Introduction
01:05 Meet Karl Simon and Subatomic
04:59 From Hierarchy to Intelligence Layers
07:58 Why Unified Data Changes Everything
11:10 What Companies Get Wrong with AI Adoption
13:06 Integration vs Workflow Orchestration
16:40 What AI Workflow Orchestration Looks Like
19:42 Building a Unified Data Layer
25:00 Where AI Delivers the Fastest ROI
30:47 Security and Compliance by Design
33:08 What are AI Coworkers
35:00 Managing Teams with AI Coworkers

Resources:
🔎 Find Out More About Karl Simon
Karl Simon LinkedIn
https://www.linkedin.com/in/karlsimon

Subatomic Website
https://getsubatomic.ai/

Subatomic LinkedIn
https://www.linkedin.com/company/subatomicai

Subatomic YouTube
https://www.youtube.com/channel/UCvluGpd82E00q-s-wXBx4FQ
🛠 AI Tools and Resources Mentioned:
Subatomic 
https://getsubatomic.ai

ChatGPT
https://chatgpt.com

Microsoft Copilot
https://copilot.microsoft.com

Block
https://block.xyz

Sequoia Capital
https://www.sequoiacap.com

Nate B. Jones YouTube
https://www.youtube.com/@NateBJones

Vistage 
https://www.vistage.com

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Most manufacturers are chasing the wrong AI problem. In this episode Chris talks with Bryan DeBois, Director of Industrial AI at RoviSys, about why industrial AI for manufacturing requires a different approach than generative AI.
Bryan explains the limits of generative AI on the plant floor, why deterministic systems matter in high risk environments, and how analytical AI, predictive AI, computer vision, and autonomous AI are already being used to improve quality, safety, throughput, and asset performance. Leaders should listen to understand how industrial AI can protect expertise, strengthen operations, and create practical advantage beyond the ChatGPT conversation.
Chapters:
00:00 Introduction
01:16 Why Factory Floor AI Is Different From Knowledge Work AI
03:58 The Four Types of AI Used in Manufacturing Today
05:50 Why Generative AI Fails in High Risk Operational Environments
10:56 Manufacturing Risks Also Apply to Construction and Life Sciences
11:36 The Workforce Crisis Driving Industrial AI Adoption
15:26 Why Manufacturing Careers May Be Safer Than White Collar Jobs
20:42 Why Humanoid Robots Are Not the Future of Manufacturing
24:53 Capturing Tribal Knowledge Before Experts Retire
40:00 Who Should Own AI Inside Manufacturing Organizations
43:24 Meta’s Cicero Project and the Future of Hybrid AI Systems
47:08 Deterministic AI vs Probabilistic AI in Critical Industries
49:27 Where to Follow Brian De Bois and Learn More About Industrial AI

Resources:
🔎 Find Out More About Bryan DeBois
Bryan DeBois on LinkedIn:
https://www.linkedin.com/in/bryan-debois
RoviSys Industrial AI: 
https://www.rovisys.com/ai
RoviSys:
https://www.rovisys.com
🛠 AI Tools and Resources Mentioned:
ChatGPT:
https://chatgpt.com/

Claude:
https://claude.com/

Grok:
https://grok.com/

Meta AI CICERO:
https://ai.meta.com/research/cicero

Google DeepMind AlphaGo:
https://deepmind.google/research/breakthroughs/alphago

Microsoft HoloLens:
https://www.microsoft.com/hololens

Obsidian:
https://obsidian.md

SAP:
https://www.sap.com

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Most AI strategies fail because the organization never changes. In this episode Chris sits down with Melissa Reeve, creator of the Hyperadaptive Model and author of an upcoming book on AI-native organizations, to explore why legacy structures block AI progress and what leaders must redesign to unlock real value.
They discuss how companies can move from siloed, handoff-heavy operating models to adaptive systems built for continuous learning, faster decisions, and human-centered execution. Leaders responsible for transformation, growth, or operating performance will gain a practical lens for turning AI ambition into sustainable organizational change.

Chapters:
00:00 Introduction
00:00 Meet Melissa Reeve and the Hyperadaptive Model
00:00 Why Legacy Operating Models Limit AI Results
00:00 Moving Beyond Automation Thinking
00:00 The Shift to AI-Native Organizations
00:00 Redesigning Roles, Teams, and Workflows
00:00 Building a Human-Centered AI Transformation Strategy
00:00 Creating Continuous Learning Systems
00:00 How Leaders Scale AI Adoption Across the Business
00:00 What the Future Organization Looks Like

🔎 Find Out More About Melissa Reeve
Melissa Reeve LinkedIn 
https://www.linkedin.com/in/melissamreeve
Hyperadaptive Solutions
http://hyperadaptive.solutions
Book Waitlist
https://hyperadaptive.solutions/book
Blueprint Session
https://hyperadaptive.solutions/why-us#contactForm
🛠 AI Tools and Resources Mentioned:
AI Integration Guide
http://hyperadaptive.solutions
AI Learning Flywheel Ebook
http://hyperadaptive.solutions/flywheel-ebook
Applied AI Workshop 
http://hyperadaptive.solutions/labs